3.3 The minimum polynomial

  • Proposition 3.5. Let \(A\in M_n(\F )\). Then there is a monic polynomial \(p\in \F [x]\) such that \(p(A)=0\).

    Similarly, if \(\phi \in L(V)\) is a linear operator on a finite-dimensional vector space over \(\F \) then there is a monic polynomial \(p\in \F [x]\) with \(p(\phi )=0\).

  • Proof. We prove the result for \(A\) and then deduce that for \(\phi \).

    We know that \(\dim M_n(\F )=n^2\) so that the \(n^2+1\) elements \(I_n,A,\dots ,A^{n^2}\) of \(M_n(\F )\) must be linearly dependent. We therefore have a linear relation

    \begin{equation*} a_0I_n+\dots +a_{n^2}A^{n^2}=0 \end{equation*}

    with not all \(a_k\) zero. Otherwise said, \(q(A)=0\), where

    \begin{equation*} q=a_0+\dots +a_{n^2}x^{n^2}\in \F [x]. \end{equation*}

    Let \(a_{m}\) be the leading term of \(q\) (\(m\) could be less than \(n^2\)). Then \(p:=q/a_m\) is a monic polynomial with \(p(A)=0\).

    Now let \(\phi \in L(V)\) and let \(A\) be its matrix with respect to some basis. Let \(p\in \F [x]\) be a monic polynomial with \(p(A)=0\). Then \(p(\phi )=0\) also.  □

This prompts:

  • Definition. A minimum polynomial for \(\phi \in L(V)\), \(V\) a vector space over \(\F \) is a monic polynomial \(p\in \F [x]\) of minimum degree with \(p(\phi )=0\): thus, if \(r\in \F [x]\) has \(r(\phi )=0\) and \(\deg r< \deg p\), then \(r=0\).

    Similarly, a minimum polynomial for \(A\in M_n(\F )\) is a monic polynomial \(p\) of least degree with \(p(A)=0\).

  • Remark. If \(\phi \) has matrix \(A\) with respect to some basis, then \(p(\phi )=0\) if and only if \(p(A)=0\) so that \(p\) is a minimum polynomial for \(\phi \) if and only if it is one for \(A\).

Minimum polynomials exist and are unique:

  • Theorem 3.6. Let \(\phi \in L(V)\) be a linear operator on a finite-dimensional vector space over a field \(\F \). Then \(\phi \) has a unique minimum polynomial.

    Similarly, any \(A\in M_n(\F )\) has a unique minimum polynomial.

    We denote these by \(m_{\phi }\) and \(m_A\) respectively.

  • Proof. We prove this for \(\phi \). The argument for \(A\) is the same.

    By Proposition 3.5, the set of non-zero polynomials which vanish on \(\phi \) is non-empty. Choose one of smallest degree and divide by the leading term if necessary to get a monic one. This settles existence.

    For uniqueness, suppose that we have \(p_1,p_2\) in the set, both monic and of smallest degree. Set \(r=p_1-p_2\). Then \(\deg r<\deg p_{i}\), since the leading terms of the \(p_{i}\) cancel, while \(r(\phi )=p_1(\phi )-p_2(\phi )=0\). Thus \(r=0\) and \(p_1=p_2\).  □

  • Remark. Unless \(V=\set {0}\), \(\deg m_{\phi }\geq 1\): the only monic polynomial of degree zero is \(1\) and \(1(\phi )=\id _V\neq 0\)!

  • Examples.

    • (1) \(m_0=x\).

    • (2) \(m_{\id _V}=x-1\).

    • (3) More generally, for \(\lambda \in \F \), \(m_{\lambda \id _V}=x-\lambda \). Thus \(\deg m_{\phi }=1\) if and only if \(\phi =\lambda \id _{V}\), for some \(\lambda \in \F \).

    • (4) Let \(\pi \in L(V)\) be a projection with \(0<\dim \ker \pi <\dim V\). Then \(m_{\pi }=x^2-x\) (exercise!).

How can we compute \(m_A\)? One method is to find it by brute force: for each \(k\geq 1\) in turn, seek \(\lst {a}0{k-1}\) such that

\begin{equation*} a_0I+\dots +a_{k-1}A^{k-1}+A^k=0. \end{equation*}

This is \(n^2\) inhomogeneous linear equations in \(k\) unknowns. They are either inconsistent, in which case you move on to \(k+1\) or, the first time you find a solution, \(m_{A}=a_0+\dots +x^k\).

  • Examples.

    • (1) Find \(m_A\) where

      \begin{equation*} A= \begin{pmatrix} 1&2\\3&4 \end {pmatrix}. \end{equation*}

      Solution. \(A\neq \lambda I\) so \(\deg m_{A}\geq 2\). First try to find \(a_0,a_1\) with \(a_0I+a_1A+A^2=0\). This expands out to

      \begin{equation*} \begin{pmatrix} a_0+a_1+7&0+2a_1+10\\0+3a_1+15&a_0+4a_1+22 \end {pmatrix}=0 \end{equation*}

      The equation in the \((1,2)\)-slot gives \(a_1=-5\) and then that in the \((1,1)\)-slot gives \(a_0=-2\). These also satisfy the other two equations and so \(m_A=-2-5x+x^2\).

    • (2) Find \(m_A\) where

      \begin{equation*} A= \begin{pmatrix} 0&1&0\\0&0&1\\1&0&0 \end {pmatrix}. \end{equation*}

      Solution. We have

      \begin{equation*} A^{2}= \begin{pmatrix} 0&0&1\\1&0&0\\0&1&0 \end {pmatrix} \end{equation*}

      so that the \((1,3)\)-slot of \(a_0I_3+a_1A+A^2=0\) gives the inconsistent equation \(a_00+a_10+1=0\) and we conclude that \(\deg m_A\) is at least three. Carrying on, we compute \(A^3\) and find that \(A^3=I_3\) which short-circuits the whole story: \(A^3-I_3=0\) so that \(m_A=x^3-1\).

We will see other ways to compute the minimum polynomial later.

One reason the minimum polynomial is important:

  • Proposition 3.7. Let \(\phi \in L(V)\) be a linear operator on a finite-dimensional vector space over \(\F \) and \(p\in \F [x]\).

    Then \(p(\phi )=0\) if and only if \(m_{\phi }\) divides \(p\), that is, there is \(s\in \F [x]\) such that \(p=sm_{\phi }\).

  • Proof. If \(p(\phi )=0\) then, by Theorem 3.1, there are \(s,r\in \F [x]\) with \(\deg r<\deg m_{\phi }\) such that \(p=sm_{\phi }+r\). But then

    \begin{equation*} 0=p(\phi )=s(\phi )m_{\phi }(\phi )+r(\phi )=r(\phi ) \end{equation*}

    so that \(r=0\) and \(p=sm_{\phi }\) by the smallest degree property of \(m_{\phi }\).

    Conversely, if \(p=sm_{\phi }\) then \(p(\phi )=s(\phi )m_{\phi }(\phi )=0\).  □

Of course, the same statement (and proof!) holds for the minimum polynomial of a matrix \(A\in M_n(\F )\).